The Dual Problem
(Ekeland and Temam 1999) describe the concept of perturbed optimization and how it relates to duality theory. It is interesting to derive standard Lagrange and Fenchel duality from this perspective. Consider a function from to the extended real numbers . We are interested minimizing . We introduce a perturbation function where and such that
Our purpose is to compute where the value function is defined as
The convex conjugate function is defined as
so that for all . In particular, we have
for all and . Since algebra directly shows that , we have the fundamental inequality:
This is the so-called dual problem. Note that it does indeed depend on the perturbation function and not only on the function . If one can analyze the conjugate function , then one can obtain lower bounds on the infimum of . As first sight, Equation 1 may seem like a trivial result since for any function we have that and getting hold of the conjugate function does not seem to be an easy task; we will see that this is not the case. One says that strong duality holds if
Convexity and Strong Duality
If one assumes that the objective function is convex and the perturbation function is jointly convex in , then the value function is also convex. Indeed, the function is convex as soon as the function is convex in . Since a function equals its biconjugate at a point as soon its subdifferential is nonempty, this shows that strong duality @#eq-strong-duality holds as soon as . Furthermore, in a finite dimensional space, a convex function is continuous and admits a subdifferential at any point within the interior of its domain. This shows that if belongs to the interior of the domain of , then strong duality holds. Since a convex function is continuous at if it is bounded in a neighborhood of , strong duality holds as soon as is bounded in a neighborhood of . By definition of the value function we have that . This shows that strong duality holds as soon is finite and there exists such that the function is continuous at .
Fenchel Duality
Consider the case of a function to minimize of the type:
for a linear operator and two convex functions and . One can consider the perturbation function
Algebra gives that so that the dual problem reads
By the previous discussion, strong duality holds, for example, as soon as the primal problem is finite and there exists such that and the function is continuous at .
References
Ekeland, Ivar, and Roger Temam. 1999. Convex Analysis and Variational Problems. SIAM.